Carbon emission efficiency identification and evaluation method and system based on cultivated land transformation

Abstract

The invention provides a carbon emission efficiency identification and evaluation method based on cultivated land transformation. The method comprises the following steps: firstly, collecting basic data of multi-source cultivated land transformation and carbon emission, and carrying out standardization processing such as space-time matching and abnormal value correction; constructing an evaluation system of four types of indexes including carbon emission source and sink, transformation characteristics and the like, and determining weights by using an analytic hierarchy process and a variable coefficient combination method; the net carbon emission is calculated through a transformation stage dynamic coefficient method, and a super-efficiency SBM model containing adjusting parameters is constructed to calculate an efficiency value; and generating a comprehensive evaluation result in combination with kernel density estimation and K-means clustering division efficiency levels. The method fits the dynamic characteristics and regional differences of the cultivated land transformation complete period, solves the problems of low distinction degree and insufficient practicability, improves the accuracy and pertinence of carbon emission efficiency identification and evaluation, and provides scientific guidance for cultivated land carbon emission management and control.

Description

A method and system for identifying and evaluating carbon emission efficiency based on farmland transformation Technical Field

[0001] This invention relates to the field of ecological and low-carbon development technology, and in particular to a method and system for identifying and evaluating carbon emission efficiency based on farmland transformation. Background Technology

[0002] With the intensification of global climate change and the tightening of land resource constraints, the transformation process of arable land, as the core carrier of agricultural production and an important carbon source and sink unit, has an increasingly significant impact on the total amount and efficiency of regional carbon emissions.

[0003] Currently, farmland transformation assessments mostly focus on explicit characteristics such as quantitative changes and spatial structure adjustments. Some studies quantify the degree of transformation by constructing an indicator system that includes production, living, and ecological functions, but lack characterization of the dynamic differences across the entire transformation cycle. Carbon emission accounting mainly adopts the fixed coefficient method recommended by the IPCC, which identifies carbon emission sources such as fertilizers, pesticides, and agricultural machinery and calculates the total amount by combining coefficients, without fully considering the dynamic impact of ecological sensitivity during the transformation stage and regional natural environment differences on emission coefficients. Carbon emission efficiency assessments are mostly based on the traditional super-efficiency SBM model, with production inputs and economic outputs as core variables, without considering key moderating factors such as differences in farmland transformation types and ecological adaptation coefficients. The input and output variables are not set in a way that fully fits the particularities of farmland transformation, and the efficiency values ​​cannot truly reflect the level of green utilization after transformation. At the same time, existing graded assessments mostly rely on a single efficiency value quantile to classify levels, without combining multiple dimensions such as net carbon emissions and transformation adaptation, resulting in low differentiation and insufficient practicality of the assessment results, and failing to provide accurate guidance for farmland carbon emission management for different transformation types and regions. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for identifying and evaluating carbon emission efficiency based on farmland transformation. By constructing a multi-dimensional evaluation index system, calculating the super-efficiency SBM model, and conducting hierarchical evaluation, the accuracy and practicality of carbon emission efficiency identification and evaluation can be improved.

[0005] To achieve the above objectives, this invention provides the following solution: a method for identifying and evaluating carbon emission efficiency based on farmland transformation, comprising the following steps: collecting multi-source farmland transformation data and basic carbon emission data, and performing spatiotemporal matching, outlier correction, and standardization on the multi-source farmland transformation data and basic carbon emission data to obtain a standardized basic dataset; constructing an evaluation index system for carbon emission efficiency based on farmland transformation, and determining the weight of each index in the evaluation index system through the analytic hierarchy process and the coefficient of variation combination method; the evaluation index system includes: carbon emission source and sink indicators, farmland transformation characteristic indicators, efficiency-driving indicators, and ecological constraint indicators; through transformation... The dynamic coefficient method for the transition phase is used to calculate carbon emissions and carbon sinks during the farmland transition process. Net carbon emissions are calculated by combining a standardized basic dataset and an evaluation index system. Net carbon emissions are treated as undesirable outputs, while farmland output benefits are treated as desired outputs. A super-efficiency SBM model is constructed, and carbon emission efficiency values ​​during the farmland transition process are calculated using the super-efficiency SBM model. Efficiency levels are classified according to the distribution characteristics of carbon emission efficiency values, and evaluation criteria for efficiency levels are determined to obtain carbon emission efficiency grading evaluation rules. A comprehensive result for carbon emission efficiency identification and evaluation is generated based on net carbon emissions, carbon emission efficiency values, and carbon emission efficiency grading evaluation rules.

[0006] Optionally, multi-source farmland transformation data includes: area data and time data of farmland conversion to construction land, farmland conversion to forest land, farmland conversion to grassland, and internal structural adjustments of farmland; basic carbon emission data includes: carbon emission source data, socio-economic data, and natural environment data; carbon emission source data includes: carbon source data of fertilizer application, carbon source data of straw burning, carbon source data of agricultural machinery operation, carbon sequestration and carbon sink data of farmland vegetation, and carbon sequestration and carbon sink data of soil; socio-economic data includes: regional GDP, agricultural population, and output value per mu of farmland; natural environment data includes: average annual precipitation, soil organic matter content, and topographic slope.

[0007] Optionally, multi-source farmland transformation data and carbon emission baseline data are collected, and spatiotemporal matching, outlier correction, and standardization are performed on the multi-source farmland transformation data and carbon emission baseline data to obtain a standardized baseline dataset. This includes: unifying the multi-source farmland transformation data and carbon emission baseline data to the same spatiotemporal resolution, converting them to the same coordinate system through Gaussian projection, and supplementing missing data values ​​through linear interpolation; correcting outliers in the multi-source farmland transformation data and carbon emission baseline data through a neighborhood weighted correction method, and replacing outliers according to the weight of the neighborhood data; and normalizing the multi-source farmland transformation data and carbon emission baseline data through an extreme value standardization method.

[0008] Optionally, carbon emission source and sink indicators include: carbon emissions, carbon sinks, and net carbon emissions; farmland transformation characteristic indicators include: transformation rate, transformation degree, and transformation direction; efficiency-driven indicators include: fertilizer application intensity, agricultural machinery input intensity, and straw return rate; and ecological constraint indicators include: soil erosion modulus, vegetation cover, and farmland quality grade.

[0009] Optionally, the carbon emissions and carbon sinks during the farmland transition process can be calculated using the dynamic coefficient method of the transition stage, and the net carbon emissions can be calculated by combining the standardized basic dataset and evaluation index system. This includes: dividing the entire farmland transition cycle into three different dynamic stages based on the farmland transition time data and vegetation cover data in the standardized basic dataset; the dynamic stages include: the initial transition stage, the middle transition stage, and the stable transition stage; calculating the ecological sensitivity weight of the dynamic stages based on ecological constraint indicators; calculating the carbon emission dynamic adjustment coefficient based on the duration of the dynamic stages and the ecological sensitivity weight; calculating the carbon emissions and carbon sinks in stages based on the carbon emission dynamic adjustment coefficient; and verifying the carbon emissions and carbon sinks numerically using carbon emission source and sink indicators. If the verification is successful, the difference between the carbon emissions and carbon sinks is taken as the net carbon emissions; if the verification fails, the ecological sensitivity weight is recalculated.

[0010] Optionally, a super-efficient SBM model is constructed, treating net carbon emissions as the undesirable output and farmland output benefits as the desired output. The carbon emission efficiency value during farmland transition is calculated using this super-efficient SBM model, which includes: an input indicator set, a desired output indicator set, undesirable output indicators, and adjustment parameters; the input indicator set includes: basic production inputs and transition-specific inputs; the desired output indicator set includes: farmland output benefits and ecological outputs; the undesirable output indicators are obtained by multiplying net carbon emissions by the transition type influence coefficient; the adjustment parameters include: transition type weights and ecological fit coefficients; an objective function is constructed based on the super-efficient SBM model; the expression for the objective function is: ;in, This represents the carbon emission efficiency value. The number of indicators in the input indicator set. The number of indicators in the expected output indicator set. Let i be the value of the input indicator for the k-th decision-making unit. Let be the value of the i-th input indicator of the decision-making unit to be evaluated. Let j be the expected output index value of the k-th decision-making unit. Let j be the expected output index value of the decision-making unit to be evaluated. Let be the undesired output index value of the k-th decision-making unit. The values ​​of the undesired output indicators of the decision-making unit to be evaluated are: Weighting for transformation types, The ecological adaptation coefficient is used; the objective function is solved through differentiated constraints to obtain the initial efficiency value; the differentiated constraints include: input constraints, expected output constraints, undesired output constraints, and weight and slack variable constraints; the initial efficiency value is calibrated with upper and lower limits to obtain the carbon emission efficiency value.

[0011] Optionally, the initial efficiency value is calibrated with upper and lower limits to obtain the carbon emission efficiency value, including: when the initial efficiency value > 1.2, the carbon emission efficiency value is calibrated with an upper limit; the calculation formula for the carbon emission efficiency value after upper limit calibration is: ,in The initial efficiency value is used; when the initial efficiency value is <0.1, the carbon emission efficiency value is calibrated to a lower limit; the formula for calculating the carbon emission efficiency value after lower limit calibration is: .

[0012] Optionally, based on the distribution characteristics of carbon emission efficiency values, efficiency levels are classified and evaluation criteria for efficiency levels are determined to obtain carbon emission efficiency grading evaluation rules. These rules include: performing kernel density estimation and dynamic quantile analysis on carbon emission efficiency values ​​to obtain core quantiles; dividing net carbon emissions into three clusters using K-means clustering and constructing a cross-distribution matrix between the clusters and carbon emission efficiency values; the clusters include: low carbon emissions, medium carbon emissions, and high carbon emissions; determining the grading critical point based on the core quantiles and the cross-distribution matrix; and constructing carbon emission efficiency grading evaluation rules based on the grading critical point and the cross-distribution matrix.

[0013] A carbon emission efficiency identification and evaluation system based on farmland transformation includes: a data acquisition module for collecting multi-source farmland transformation data and basic carbon emission data, and performing spatiotemporal matching, outlier correction, and standardization on the multi-source farmland transformation data and basic carbon emission data to obtain a standardized basic dataset; a system construction module for constructing an evaluation index system for carbon emission efficiency based on farmland transformation, and determining the weight of each index in the evaluation index system through the analytic hierarchy process and the coefficient of variation combination method; the evaluation index system includes: carbon emission source and sink indicators, farmland transformation characteristic indicators, efficiency-driving indicators, and ecological constraint indicators; and a carbon emission calculation module for calculating the carbon emission efficiency using the dynamic coefficient method of the transformation stage. The system calculates net carbon emissions by analyzing carbon emissions and carbon sinks during farmland transition, using a standardized dataset and evaluation index system. An efficiency calculation module treats net carbon emissions as an undesirable output and farmland output benefits as the desired output, constructing a super-efficiency SBM model to calculate carbon emission efficiency values ​​during farmland transition. An identification and evaluation module classifies efficiency levels based on the distribution characteristics of carbon emission efficiency values ​​and determines evaluation criteria for each level, resulting in carbon emission efficiency grading evaluation rules. A result generation module generates a comprehensive carbon emission efficiency identification and evaluation result based on net carbon emissions, carbon emission efficiency values, and the carbon emission efficiency grading evaluation rules.

[0014] According to specific embodiments provided by the present invention, the following technical effects are disclosed: The present invention provides a method and system for identifying and evaluating carbon emission efficiency based on farmland transformation. The method includes: collecting multi-source farmland transformation data and basic carbon emission data, and performing spatiotemporal matching, outlier correction, and standardization on the multi-source farmland transformation data and basic carbon emission data to obtain a standardized basic dataset; constructing an evaluation index system for carbon emission efficiency based on farmland transformation, and determining the weight of each index in the evaluation index system through the combination of analytic hierarchy process and coefficient of variation; calculating the carbon emissions and carbon sinks during the farmland transformation process using the dynamic coefficient method of the transformation stage, and calculating the net carbon emissions by combining the standardized basic dataset and the evaluation index system; constructing a super-efficiency SBM model by treating net carbon emissions as the undesired output and farmland output benefits as the desired output, and calculating the carbon emission efficiency value during the farmland transformation process through the super-efficiency SBM model; classifying efficiency levels according to the distribution characteristics of carbon emission efficiency values ​​and determining the evaluation criteria for efficiency levels to obtain carbon emission efficiency grading evaluation rules; and generating a comprehensive result for carbon emission efficiency identification and evaluation based on net carbon emissions, carbon emission efficiency values, and carbon emission efficiency grading evaluation rules. This method aligns with the dynamic characteristics and regional differences throughout the entire cycle of farmland transformation, solves the problems of low differentiation and insufficient practicality, improves the accuracy and relevance of carbon emission efficiency identification and evaluation, and provides scientific guidance for farmland carbon emission management. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 is a flowchart of the carbon emission efficiency identification and evaluation method of the present invention; Figure 2 is a schematic diagram of the carbon emission efficiency identification and evaluation system of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] As shown in Figure 1, this invention provides a method for identifying and evaluating carbon emission efficiency based on farmland transformation, comprising the following steps: Step 100: Collect multi-source farmland transformation data and basic carbon emission data, and perform spatiotemporal matching, outlier correction, and standardization on the multi-source farmland transformation data and basic carbon emission data to obtain a standardized basic dataset; Step 200: Construct an evaluation index system for carbon emission efficiency based on farmland transformation, and determine the weight of each index in the evaluation index system through the analytic hierarchy process and the coefficient of variation combination method; The evaluation index system includes: carbon emission source and sink indicators, farmland transformation characteristic indicators, efficiency-driven indicators, and ecological constraint indicators; Step 300: Through the transformation stage dynamics... The carbon emission and carbon sink during the farmland transition process are calculated using the state coefficient method, and the net carbon emission is calculated by combining the standardized basic dataset and evaluation index system; Step 400: The net carbon emission is treated as the undesired output and the farmland output benefit is treated as the desired output. A super-efficiency SBM model is constructed, and the carbon emission efficiency value during the farmland transition process is calculated using the super-efficiency SBM model; Step 500: The efficiency level is divided according to the distribution characteristics of the carbon emission efficiency value, and the evaluation criteria for the efficiency level are determined to obtain the carbon emission efficiency classification evaluation rules; Step 600: The comprehensive result of carbon emission efficiency identification and evaluation is generated based on the net carbon emission, carbon emission efficiency value, and carbon emission efficiency classification evaluation rules.

[0020] Preferably, the multi-source farmland transformation data includes: area data and time data of farmland conversion to construction land, farmland conversion to forest land, farmland conversion to grassland, and internal structural adjustments of farmland; the basic carbon emission data includes: carbon emission source data, socio-economic data, and natural environment data; the carbon emission source data includes: carbon source data of fertilizer application, carbon source data of straw burning, carbon source data of agricultural machinery operation, carbon sequestration and carbon sink data of farmland vegetation, and carbon sequestration and carbon sink data of soil; the socio-economic data includes: regional GDP, agricultural population, and output value per mu of farmland; the natural environment data includes: average annual precipitation, soil organic matter content, and topographic slope.

[0021] Preferably, multi-source farmland transformation data and carbon emission baseline data are collected, and spatiotemporal matching, outlier correction, and standardization are performed on the multi-source farmland transformation data and carbon emission baseline data to obtain a standardized baseline dataset. This includes: unifying the multi-source farmland transformation data and carbon emission baseline data to the same spatiotemporal resolution, converting them to the same coordinate system through Gaussian projection, and supplementing missing data values ​​through linear interpolation; correcting outliers in the multi-source farmland transformation data and carbon emission baseline data through a neighborhood weighted correction method, and replacing outliers according to the weight of the neighborhood data; and normalizing the multi-source farmland transformation data and carbon emission baseline data through an extreme value standardization method.

[0022] In the specific implementation process, step 100 unifies the spatial resolution of multi-source farmland transformation data and carbon emission baseline data to a 30m×30m raster scale and the temporal resolution to an annual scale. Then, Gaussian projection transformation is performed. First, the WGS-84 Gauss-Kruger 3-degree zone projection is selected as the target coordinate system. The projection parameters of different original coordinate system data are analyzed using a projection transformation algorithm to complete coordinate system unification. For missing values ​​in the spatiotemporal sequence, a linear interpolation method is used. Based on 2-3 adjacent valid data points before and after the missing data point, a linear fitting function y=ax+b is constructed. Here, a is obtained by calculating the slope through the difference between adjacent data points, and b is obtained by substituting the coordinates of valid data points to solve for the intercept, thereby estimating and supplementing the missing values. For outliers in the data, a neighborhood weighted correction method is used. First, a 3×3 raster spatial neighborhood and a temporal neighborhood for the three preceding and following time periods are defined. Then, a distance decay function w=1 / d is used to correct the outliers. 2 The weight of each valid data point within the neighborhood is calculated, where d is the spatiotemporal distance between the neighborhood data and the outlier. A weighted average formula is then used to calculate the correction value, which replaces the original outlier. Finally, extreme value standardization is employed. For each indicator, the minimum and maximum values ​​are first selected, and then all original data undergo min-max normalization to map the data uniformly to the [0,1] interval, eliminating dimensional differences between different indicators and ultimately obtaining a standardized basic dataset.

[0023] In the specific implementation process, step 200 first systematically sorts out the core impact dimensions of farmland transformation on carbon emission efficiency, selects indicators that are scientific, systematic, and operable, and clarifies that the indicator system includes four criterion layers. Among them, carbon emission source and sink indicators cover carbon emissions, carbon sinks, and net carbon emissions; farmland transformation characteristic indicators include transformation rate, transformation degree, and transformation direction; efficiency-driven indicators involve fertilizer application intensity, agricultural machinery input intensity, and straw return rate; and ecological constraint indicators include soil erosion modulus, vegetation coverage, and farmland quality grade. Specific indicators are further refined under each criterion layer, and the definitions and data sources of the indicators are clarified. Subsequently, the analytic hierarchy process (AHP) and the coefficient of variation combination method are used to determine the indicator weights. The AHP part first constructs a hierarchical structure of target layer (farmland transformation carbon emission efficiency evaluation) - criterion layer (four types of core indicators) - indicator layer (specific indicators). Five to eight experts in the fields of ecological environment and land use are invited to conduct pairwise importance comparisons of indicators at the same level using the 1-9 scaling method, constructing a judgment matrix A, and calculating the maximum eigenvalue λ of the judgment matrix using the sum-product method. max and the corresponding normalized eigenvector W ahp (i.e., subjective weighting), and perform a consistency test, the test formula is: ; ;in This is a consistency indicator, where n is the number of indicators at the same level. The random consistency index is obtained by querying the standard RI table based on the value of n. If the value is less than 0.1, the judgment matrix meets the consistency requirement; otherwise, feedback is needed to the experts to adjust the judgment matrix until the test passes. The coefficient of variation method first calculates the mean and standard deviation of each indicator based on the standardized basic dataset, and uses the ratio of the two as the coefficient of variation. The coefficient of variation is then normalized to obtain the objective weights. Finally, a weighted average method is used to integrate the subjective and objective weights, with a weighting coefficient of 0.5 for both weights to balance subjective expert experience with objective data characteristics, ultimately determining the specific weights of all indicators in the evaluation indicator system.

[0024] Preferably, the carbon emissions and carbon sinks during the farmland transformation process are calculated using the dynamic coefficient method of the transformation stage, and the net carbon emissions are calculated by combining the standardized basic dataset and evaluation index system. This includes: dividing the entire farmland transformation cycle into three different dynamic stages based on the farmland transformation time data and vegetation cover data in the standardized basic dataset; the dynamic stages include: the initial transformation stage, the middle transformation stage, and the stable transformation stage; calculating the ecological sensitivity weight of the dynamic stages based on ecological constraint indicators; calculating the carbon emission dynamic adjustment coefficient based on the duration of the dynamic stages and the ecological sensitivity weight; calculating the carbon emissions and carbon sinks in stages based on the carbon emission dynamic adjustment coefficient; and verifying the carbon emissions and carbon sinks numerically using carbon emission source and sink indicators. If the verification is successful, the difference between the carbon emissions and carbon sinks is taken as the net carbon emissions; if the verification fails, the ecological sensitivity weight is recalculated.

[0025] In the specific implementation process, step 300 first divides the dynamic stages of farmland transformation, and calculates the rate of change of vegetation cover based on the farmland transformation time data and vegetation cover data in the standardized basic dataset. , Let be the vegetation cover at time t. The vegetation cover at the start of the transformation, when A transformation is considered to be in its initial stage if the percentage of people with more than 30% of their population and the transformation period is ≤3 years. A transformation is considered to be in its initial stage if the percentage of people with more than 10% of their population and the transformation period is ≤3 years. A transition period is defined as the middle stage when the percentage of people with disabilities is ≤30% and the transition duration is ≤8 years (3 years < 3 years). The transition period is defined as stable when the percentage of soil erosion modulus (E), vegetation cover (VC), and farmland quality grade (Q) is less than 10% and the transition duration is greater than 8 years. Subsequently, the ecological sensitivity weights for the dynamic stages are calculated. First, the extreme value standardization method is used to standardize soil erosion modulus (E), vegetation cover (VC), and farmland quality grade (Q) into positive indicator values. , , Then, the weights of each indicator are calculated using the entropy weight method. The formula for calculating the entropy weight is: ;in, Let be the entropy value of the i-th ecological constraint index. For the sample size, This represents the weight of the i-th indicator in the k-th sample. Ecological sensitivity weight. Ecological sensitivity weight in the final stage .

[0026] Next, the dynamic adjustment factor for carbon emissions is calculated using the following formula: ;in, Let t be the duration of stage t. This represents the total timeframe for the entire farmland transition cycle. Then, carbon emissions and carbon sinks are calculated in stages, using the following formulas: ; ;in, Let i be the carbon emissions of the i-th type of transition in stage t. For the i-th type of transition, the basic carbon emission coefficient, For the area of ​​the i-th type of transformation, Let J be the carbon sink amount of the j-th type of carbon sink in stage t. Let J be the basic carbon sink coefficient for the j-th type of carbon sink. This represents the area corresponding to the j-th type of carbon sink. Finally, numerical verification is performed. Using the theoretical carbon emission range and theoretical carbon sink range in the carbon emission source and sink indicators as standards, it is determined whether the total carbon emissions calculated in stages fall within the corresponding ranges, and whether the relative error is ≤5%. If the verification is successful, the difference between carbon emissions and carbon sinks is taken as the net carbon emissions. If it fails, the ecological sensitivity weight and subsequent carbon emissions and carbon sinks are recalculated until the verification is successful.

[0027] Preferably, net carbon emissions are taken as the undesired output and farmland output benefits are taken as the desired output. A super-efficient SBM model is constructed, and the carbon emission efficiency value during the farmland transformation process is calculated using the super-efficient SBM model. This includes: constructing the super-efficient SBM model; constructing an objective function based on the super-efficient SBM model; solving the objective function through differentiated constraints to obtain the initial efficiency value; the differentiated constraints include: input constraints, desired output constraints, undesired output constraints, and weight and slack variable constraints; and calibrating the initial efficiency value with upper and lower limits to obtain the carbon emission efficiency value.

[0028] In the specific implementation process, step 400 first clarifies the core components of the super-efficiency SBM model, including: an input indicator set, an expected output indicator set, undesirable output indicators, and adjustment parameters. The input indicator set X includes: basic production inputs and transformation-specific inputs. Basic production inputs include: labor force quantity, fertilizer application rate, and total agricultural machinery power. Transformation-specific inputs include: farmland transformation and remediation costs and ecological restoration investment amounts. The input indicator set contains a total of m indicators, denoted as X = [x1, x2, ..., x...]. m ], where x iLet be the standardized value of the i-th input. The expected output indicator set Y includes: arable land output benefits and ecological output. Arable land output benefits cover economic output, including: agricultural output value per mu of arable land and grain yield per unit area. Ecological output includes: carbon sink benefits per unit area and ecological service value of arable land. The expected output indicator set has a total of s indicators, denoted as Y=[y1,y2,...,y...]. s ], where y j Let be the standardized value of the j-th expected output. The undesirable output indicator Z is based on net carbon emissions, multiplied by the transition type impact coefficient. Obtain, represented as , Net carbon emissions, when arable land is converted to construction land =1.2 When converting cultivated land to forest land =0.3, when converting farmland to grassland =0.5, during internal adjustments of cultivated land =0.8. Adjustment parameters include: transformation type weight and ecological fit coefficient. The transformation type weight is calculated based on the degree of impact of the transformation on the regional ecosystem service value, using the following formula: , The change in ecosystem service value and ecological adaptability coefficient of the first-class transformation of cultivated land output benefit t. Farmland output benefits based on standardized values ​​of soil organic matter content and standardized values ​​of vegetation cover The weighted average is obtained, and the calculation formula is as follows: Next, we construct the objective function, whose expression is: ;in, This represents the carbon emission efficiency value. The number of indicators in the input indicator set. The number of indicators in the expected output indicator set. Let i be the value of the input indicator for the k-th decision-making unit. Let be the value of the i-th input indicator of the decision-making unit to be evaluated. Let j be the expected output index value of the k-th decision-making unit. Let j be the expected output index value of the decision-making unit to be evaluated. Let be the undesired output index value of the k-th decision-making unit. The values ​​of the undesired output indicators of the decision-making unit to be evaluated are: Weighting for transformation types, This is the ecological adaptability coefficient. The numerator is calculated using... Item to improve input efficiency The bigger The more reasonable the calculation, the better the adaptability to the transformation, the lower the penalty weight for redundant investment, and the better the denominator. This enhances the expected output contribution of highly adaptable transformation, and through The weighting of undesirable outputs from high-carbon emission transition types is amplified. Subsequently, differentiated constraints are set, with input constraints as follows: ,in Let k be the weight coefficient of the k-th decision-making unit for the output benefit of cultivated land. Let i be the slack variable (representing input redundancy) for the i-th cultivated land output benefit term, and That is, the worse the ecological adaptability, the lower the allowable threshold for input redundancy. The expected output constraint is... ,in Let j be the slack variable representing the expected output of the i-th cultivated land output benefit term (indicating insufficient output), and That is, the higher the weight of the transformation type, the lower the allowable threshold for insufficient expected output. The constraint on undesirable output is... ,in These are slack variables representing undesirable outputs (and thus emissions reduction potential), and This means that the emission reduction potential constraints for high-carbon emission transition types are more stringent. The weighting and slack variable constraints are... , , , and Finally, the simplex method from linear programming is used to solve the objective function. By transforming the fractional objective function into a linear form, the initial efficiency is obtained through iterative calculation. And perform upper and lower limit calibration, when When the value is greater than 1.2, the upper limit calibration formula is used. Perform upper limit calibration to avoid interference from extreme values; when When <0.1, the lower limit calibration formula is used. Perform a lower limit calibration to ensure the distinguishability of low efficiency values.

[0029] Preferably, the carbon emission efficiency is graded according to the distribution characteristics of the carbon emission efficiency values, and the evaluation criteria for the efficiency levels are determined to obtain the carbon emission efficiency grading evaluation rules. This includes: performing kernel density estimation and dynamic quantile analysis on the carbon emission efficiency values ​​to obtain the core quantiles; dividing net carbon emissions into three clusters using the K-means clustering method, and constructing a cross-distribution matrix between the clusters and the carbon emission efficiency values; the clusters include: low carbon emissions, medium carbon emissions, and high carbon emissions; determining the grading critical point based on the core quantiles and the cross-distribution matrix; and constructing the carbon emission efficiency grading evaluation rules based on the grading critical point and the cross-distribution matrix.

[0030] In the specific implementation process, step 500 first estimates the kernel density of the carbon emission efficiency value and uses the Silverman rule to determine the bandwidth. ,in The standard deviation of the carbon emission efficiency value. The interquartile range of the efficiency value. The sample size is given. The kernel density is then calculated using a Gaussian kernel function, and dynamic quantile analysis is performed based on the kernel density distribution. The 25th quantile (Q1), 50th quantile (Q2), and 75th quantile (Q3) are selected as core quantiles. Subsequently, K-means clustering is used to cluster net carbon emissions. The number of clusters is set to K=3, and three different net carbon emission values ​​are randomly selected as initial cluster centers. The Euclidean distance from each sample to each center is calculated, and the samples are assigned to the nearest cluster. The cluster centers are then updated using the mean of the samples within each cluster. This iteration stops when the change in cluster centers is less than 0.001, ultimately resulting in three clusters: low carbon emissions, medium carbon emissions, and high carbon emissions. Next, a cross-distribution matrix is ​​constructed, with clusters as rows and efficiency intervals defined by core quantiles as columns. The number of samples in each cell is counted, forming a 3×4 dimensional cross-distribution matrix M, where M... ij This represents the number of samples in the i-th cluster that fall within the j-th efficiency interval. Next, the grading critical points are determined, and the sample proportions of each cell are calculated. When the proportion of samples with carbon emission efficiency values ​​> Q3 in the low-carbon emission cluster is ≥60%, the proportion of samples with Q2 < carbon emission efficiency value ≤ Q3 in the high-carbon emission cluster is the highest, and the proportion of samples with carbon emission efficiency ≤ Q1 in the high-carbon emission cluster is ≥60%, the core quantiles Q1, Q2, and Q3 are determined as the grading critical points. , , Finally, combining the critical point and cluster characteristics, a four-level efficiency rating standard was defined: High Efficiency Level (… And net carbon emissions are considered carbon emissions), corresponding to a low-carbon transition with optimal efficiency; relatively high efficiency level ( And the net carbon emissions are classified as low-carbon / medium-carbon), corresponding to a low-carbon or medium-carbon transition with good efficiency; medium efficiency level ( And its net carbon emissions are classified as medium / high carbon emissions, corresponding to a transition to medium or high carbon emissions with generally low efficiency; inefficient level ( Furthermore, the net carbon emissions are classified as high carbon emissions, corresponding to a high-carbon transformation with poor efficiency, thus forming a complete carbon emission efficiency grading evaluation rule.

[0031] Specifically, the comprehensive results of carbon emission efficiency identification and evaluation in step 600 are presented in the form of tables and spatial distribution maps. The tables include farmland transformation type, net carbon emissions, carbon emission efficiency value, efficiency level and improvement suggestions, and the spatial distribution maps show the spatial distribution characteristics of farmland transformation carbon emission efficiency levels in different regions.

[0032] As shown in Figure 2, this invention also provides a carbon emission efficiency identification and evaluation system based on farmland transformation, comprising: a data acquisition module for collecting multi-source farmland transformation data and basic carbon emission data, and performing spatiotemporal matching, outlier correction, and standardization on the multi-source farmland transformation data and basic carbon emission data to obtain a standardized basic dataset; a system construction module for constructing an evaluation index system for carbon emission efficiency based on farmland transformation, and determining the weight of each index in the evaluation index system through analytic hierarchy process (AHP) and coefficient of variation combination method; the evaluation index system includes: carbon emission source and sink indicators, farmland transformation characteristic indicators, efficiency-driving indicators, and ecological constraint indicators; and a carbon emission calculation module for dynamically calculating carbon emissions during the transformation phase. The system employs a coefficient method to calculate carbon emissions and carbon sinks during farmland transition, and combines a standardized basic dataset and evaluation index system to calculate net carbon emissions. An efficiency calculation module treats net carbon emissions as an undesirable output and farmland output benefits as an expected output, constructing a super-efficiency SBM model to calculate carbon emission efficiency values ​​during farmland transition. An identification and evaluation module classifies efficiency levels based on the distribution characteristics of carbon emission efficiency values ​​and determines the evaluation criteria for each level, resulting in carbon emission efficiency grading evaluation rules. A result generation module generates a comprehensive carbon emission efficiency identification and evaluation result based on net carbon emissions, carbon emission efficiency values, and the carbon emission efficiency grading evaluation rules.

[0033] The beneficial effects of this invention are as follows: 1) By dividing the transition into early, middle, and stable phases using transition time data and vegetation cover data, and combining ecological constraint indicators to calculate ecological sensitivity weights and carbon emission dynamic adjustment coefficients, and calculating carbon emissions and carbon sinks in stages, the dynamic characteristics of the entire cycle of farmland transition are accurately depicted; 2) By constructing a multi-dimensional and scientifically sound evaluation index system, which covers four categories of indicators: carbon emission sources and sinks, farmland transition characteristics, efficiency drivers, and ecological constraints, and by integrating subjective and objective weights through hierarchical analysis and the coefficient of variation combination method, the one-sidedness of the single weight method is avoided, and the credibility and scientific nature of the index weights are improved; 3) In the super-efficiency SBM model, adjustment parameters such as the transition type influence coefficient and the ecological adaptation coefficient are introduced, clearly distinguishing between basic production inputs and transition-specific inputs, economic outputs and ecological outputs, and treating net carbon emissions as an undesirable output, solving the problem of insufficient fit between input and output variables, more accurately reflecting the green utilization level of farmland after transition, and optimizing the carbon emission efficiency calculation model. 4) By using kernel density estimation, dynamic quantile analysis, and K-means clustering, a cross-distribution matrix of net carbon emissions and efficiency values ​​was constructed, the critical point for grading was determined, and multi-dimensional grading rules were formed. This avoids the limitations of dividing by a single efficiency value quantile and can provide differentiated evaluation results for farmland with different transformation types and carbon emission levels, thus improving the distinguishability and practicality of the grading evaluation. 5) Through a series of processing methods such as unified spatiotemporal resolution, Gaussian projection transformation, linear interpolation to complete missing values, neighborhood weighted correction of outliers, and extreme value standardization and normalization, data heterogeneity and errors were eliminated, providing high-quality data support for subsequent accounting, calculation, and evaluation, and strengthening the reliability of the data foundation. 6) By combining the results generated from net carbon emissions, efficiency values, and grading rules, the efficiency level and shortcomings of individual transformation projects were clarified, and regional summary analysis and control level classification were achieved, providing a scientific basis and precise control guidance for the formulation, optimization, and adjustment of farmland transformation carbon emission control policies.

[0034] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0035] Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. Furthermore, those skilled in the art will recognize that, based on the ideas of this invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for identifying and evaluating carbon emission efficiency based on farmland transformation, characterized in that, The process includes the following steps: collecting multi-source farmland transformation data and basic carbon emission data, and performing spatiotemporal matching, outlier correction, and standardization on the multi-source farmland transformation data and basic carbon emission data to obtain a standardized basic dataset; constructing an evaluation index system for carbon emission efficiency based on farmland transformation, and determining the weight of each index in the evaluation index system using the analytic hierarchy process (AHP) and the coefficient of variation combination method; the evaluation index system includes: carbon emission source and sink indicators, farmland transformation characteristic indicators, efficiency-driven indicators, and ecological constraint indicators; and calculating the carbon emissions and carbon sinks during the farmland transformation process using the dynamic coefficient method of the transformation stage. The net carbon emissions are calculated by combining the standardized basic dataset and the evaluation index system. The net carbon emissions are treated as the undesirable output, and the farmland output benefits are treated as the desired output. A super-efficient SBM model is constructed, and the carbon emission efficiency value during the farmland transformation process is calculated using the super-efficient SBM model. Efficiency levels are classified according to the distribution characteristics of the carbon emission efficiency values, and the evaluation criteria for these efficiency levels are determined to obtain carbon emission efficiency grading evaluation rules. A comprehensive carbon emission efficiency identification and evaluation result is generated based on the net carbon emissions, the carbon emission efficiency value, and the carbon emission efficiency grading evaluation rules.

2. The carbon emission efficiency identification and evaluation method based on farmland transformation according to claim 1, characterized in that, The multi-source farmland transformation data includes: area data and time data of farmland conversion to construction land, farmland conversion to forest land, farmland conversion to grassland, and internal structural adjustments of farmland; the carbon emission basic data includes: carbon emission source data, socio-economic data, and natural environment data; the carbon emission source data includes: carbon source data of fertilizer application, carbon source data of straw burning, carbon source data of agricultural machinery operation, carbon sequestration and carbon sink data of farmland vegetation, and carbon sequestration and carbon sink data of soil; the socio-economic data includes: regional GDP, agricultural population, and output value per mu of farmland; the natural environment data includes: average annual precipitation, soil organic matter content, and topographic slope.

3. The carbon emission efficiency identification and evaluation method based on farmland transformation according to claim 1, characterized in that, Multi-source farmland transformation data and basic carbon emission data are collected, and spatiotemporal matching, outlier correction, and standardization are performed on the multi-source farmland transformation data and basic carbon emission data to obtain a standardized basic dataset. This includes: unifying the multi-source farmland transformation data and the basic carbon emission data to the same spatiotemporal resolution, converting them to the same coordinate system using Gaussian projection, and supplementing missing data values ​​using linear interpolation; correcting outliers in the multi-source farmland transformation data and the basic carbon emission data using a neighborhood weighted correction method, and replacing the outliers according to the weights of the neighborhood data; and normalizing the multi-source farmland transformation data and the basic carbon emission data using an extreme value standardization method.

4. The carbon emission efficiency identification and evaluation method based on farmland transformation according to claim 1, characterized in that, The carbon emission source and sink indicators include: carbon emissions, carbon sinks, and net carbon emissions; the farmland transformation characteristic indicators include: transformation rate, transformation degree, and transformation direction; the efficiency-driven indicators include: fertilizer application intensity, agricultural machinery input intensity, and straw return rate; the ecological constraint indicators include: soil erosion modulus, vegetation coverage, and farmland quality grade.

5. The carbon emission efficiency identification and evaluation method based on farmland transformation according to claim 1, characterized in that, The carbon emissions and carbon sinks during the farmland transition process are calculated using the dynamic coefficient method for transition stages. Net carbon emissions are then calculated by combining the standardized basic dataset and the evaluation index system. This process includes: dividing the entire farmland transition cycle into three different dynamic stages based on farmland transition time data and vegetation cover data in the standardized basic dataset; these dynamic stages include: the initial transition period, the middle transition period, and the stable transition period; calculating the ecological sensitivity weights of each dynamic stage based on the ecological constraint indicators; calculating the carbon emission dynamic adjustment coefficient based on the duration of each dynamic stage and the ecological sensitivity weights; calculating the carbon emissions and carbon sinks in stages based on the carbon emission dynamic adjustment coefficients; and verifying the carbon emissions and carbon sinks using the carbon emission source and sink indicators. If the verification is successful, the difference between the carbon emissions and carbon sinks is taken as the net carbon emissions. If the verification fails, the ecological sensitivity weights are recalculated.

6. The carbon emission efficiency identification and evaluation method based on farmland transformation according to claim 1, characterized in that, Using net carbon emissions as the undesirable output and farmland output benefits as the desired output, a super-efficient SBM model is constructed. The carbon emission efficiency value during farmland transition is calculated using this super-efficient SBM model, which includes: an input indicator set, a desired output indicator set, undesirable output indicators, and adjustment parameters; the input indicator set includes: basic production inputs and transition-specific inputs; the desired output indicator set includes: farmland output benefits and ecological outputs; the undesirable output indicators are obtained by multiplying net carbon emissions by the transition type influence coefficient; the adjustment parameters include: transition type weights and ecological fit coefficients; an objective function is constructed based on the super-efficient SBM model; the expression of the objective function is: ;in, This represents the carbon emission efficiency value. The number of indicators in the input indicator set. The number of indicators in the expected output indicator set. Let i be the value of the input indicator for the k-th decision-making unit. Let be the value of the i-th input indicator of the decision-making unit to be evaluated. Let j be the expected output index value of the k-th decision-making unit. Let j be the expected output index value of the decision-making unit to be evaluated. Let be the undesired output index value of the k-th decision-making unit. The values ​​of the undesired output indicators of the decision-making unit to be evaluated are: Weighting for transformation types, The ecological adaptation coefficient is used; the objective function is solved through differentiated constraints to obtain the initial efficiency value; the differentiated constraints include: input constraints, expected output constraints, undesired output constraints, and weight and slack variable constraints; the initial efficiency value is calibrated with upper and lower limits to obtain the carbon emission efficiency value.

7. The carbon emission efficiency identification and evaluation method based on farmland transformation according to claim 6, characterized in that, The initial efficiency value is calibrated to obtain the carbon emission efficiency value by applying upper and lower limit calibrations, including: when the initial efficiency value > 1.2, the carbon emission efficiency value is calibrated to the upper limit; the formula for calculating the carbon emission efficiency value after upper limit calibration is: ,in The initial efficiency value is used; when the initial efficiency value is <0.1, the carbon emission efficiency value is calibrated to a lower limit; the calculation formula for the carbon emission efficiency value after lower limit calibration is: 。 8. The carbon emission efficiency identification and evaluation method based on farmland transformation according to claim 1, characterized in that, Based on the distribution characteristics of the carbon emission efficiency values, efficiency levels are classified and evaluation criteria for each efficiency level are determined, resulting in carbon emission efficiency grading evaluation rules. These rules include: performing kernel density estimation and dynamic quantile analysis on the carbon emission efficiency values ​​to obtain core quantiles; dividing the net carbon emissions into three clusters using K-means clustering and constructing a cross-distribution matrix between the clusters and the carbon emission efficiency values; the clusters include: low carbon emissions, medium carbon emissions, and high carbon emissions; determining grading critical points based on the core quantiles and the cross-distribution matrix; and constructing the carbon emission efficiency grading evaluation rules based on the grading critical points and the cross-distribution matrix.

9. A carbon emission efficiency identification and evaluation system based on farmland transformation, characterized in that, include: The data acquisition module collects multi-source farmland transformation data and basic carbon emission data, and performs spatiotemporal matching, outlier correction, and standardization on the multi-source farmland transformation data and basic carbon emission data to obtain a standardized basic dataset. The system construction module constructs an evaluation index system for carbon emission efficiency based on farmland transformation, and determines the weight of each index in the evaluation index system using the analytic hierarchy process (AHP) and the coefficient of variation combination method. The evaluation index system includes: carbon emission source and sink indicators, farmland transformation characteristic indicators, efficiency-driven indicators, and ecological constraint indicators. The carbon emission calculation module calculates the carbon emissions and carbon sinks during the farmland transformation process using the dynamic coefficient method of the transformation stage, and combines the results with... The net carbon emissions are calculated by combining the standardized basic dataset and the evaluation index system; the efficiency calculation module is used to construct a super-efficiency SBM model by treating the net carbon emissions as the undesirable output and the farmland output benefits as the desired output, and to calculate the carbon emission efficiency value during the farmland transformation process through the super-efficiency SBM model; the identification and evaluation module is used to classify the efficiency levels according to the distribution characteristics of the carbon emission efficiency value and determine the evaluation criteria for the efficiency level to obtain the carbon emission efficiency classification evaluation rules; the result generation module is used to generate a comprehensive carbon emission efficiency identification and evaluation result based on the net carbon emissions, the carbon emission efficiency value and the carbon emission efficiency classification evaluation rules.

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